Mcp Rag Server

mcp-rag-server

[](https://www.npmjs.com/package/mcp-rag-server) [](LICENSE) [](package.json)

A Model Context Protocol (MCP) server that enables Retrieval Augmented Generation (RAG). It indexes your documents and serves relevant context to Large Language Models via the MCP protocol.

Integration Examples

Generic MCP Client Configuration

{
  "mcpServers": {
    "rag": {
      "command": "npx",
      "args": ["-y", "mcp-rag-server"],
      "env": {
        "BASE_LLM_API": "http://localhost:11434/v1",
        "EMBEDDING_MODEL": "nomic-embed-text",
        "VECTOR_STORE_PATH": "./vector_store",
        "CHUNK_SIZE": "500"
      }
    }
  }
}

Example Interaction

# Index documents
>> tool:embedding_documents {"path":"./docs"}

# Check status
>> resource:embedding-status

 💡 **Tip:** We recommend using [Ollama](https://ollama.com) for embedding. Install and pull the `nomic-embed-text` model:
```bash
ollama pull nomic-embed-text
export EMBEDDING_MODEL=nomic-embed-text

Configuration

| Variable | Description | Default | | ------------------- | ------------------------------------------------ | --------------------------------- | | BASE_LLM_API | Base URL for embedding API | http://localhost:11434/v1 | | LLM_API_KEY | API key for your LLM provider | (empty) | | EMBEDDING_MODEL | Embedding model identifier | nomic-embed-text | | VECTOR_STORE_PATH | Directory for local vector store | ./vector_store | | CHUNK_SIZE | Characters per text chunk (number) | 500 | > 💡 Recommendation: Use Ollama embedding models like nomic-embed-text for best performance.

Usage

MCP Tools

Once running, the server exposes these tools via MCP:

• embedding_documents(path: string): Index documents under the given path
• query_documents(query: string, k?: number): Retrieve top k chunks (default 15)
• remove_document(path: string): Remove a specific document
• remove_all_documents(confirm: boolean): Clear the entire index (confirm=true)
• list_documents(): List all indexed document paths

MCP Resources

Clients can also read resources via URIs:

• rag://documents — List all document URIs
• rag://document/{path} — Fetch full content of a document
• rag://query-document/{numberOfChunks}/{query} — Query documents as a resource
• rag://embedding/status — Check current indexing status (completed, failed, total)

How RAG Works

1. Indexing: Reads files, splits text into chunks based on CHUNK_SIZE, and queues them for embedding.
2. Embedding: Processes each chunk sequentially against the embedding API, storing vectors in SQLite.
3. Querying: Embeds the query and retrieves nearest text chunks from the vector store, returning them to the client.

Development

npm install
npm run build      # Compile TypeScript
npm start          # Run server
npm run watch      # Watch for changes

Contributing

Contributions are welcome! Please open issues or pull requests on GitHub.

License

MIT 2025 Quan Le

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: kwanLeeFrmVi
• Source: kwanLeeFrmVi/mcp-rag-server
• License: MIT

Install and usage instructions live in the source repository linked above.